code: def alltoall_ring(xs, devices, split_axis, concat_axis):
      """MPI alltoall operation.
  
      Performance-optimized for a ring of devices.
  
      Args:
        xs: a list of n tf.Tensors
        devices: a list of n strings
        split_axis: an integer
        concat_axis: an integer
  
      Returns:
        a list of n Tensors
      """
      n = len(xs)
      if n == 1:
          return xs
      # set up
      # [target, source]
      parts = [[None] * n for i in xrange(n)]
  
      def my_split(x, size_splits):
          total_size = tf.shape(x)[split_axis]
          part_size = total_size // sum(size_splits)
          return tf.split(
              x, [s * part_size for s in size_splits], axis=split_axis
          )
  
      forward_message_size = (n - 1) // 2
      backward_message_size = (n - 1) - forward_message_size
      forward_messages = [None] * n
      backward_messages = [None] * n
      for i in xrange(n):
          with tf.device(devices[i]):
              if i >= backward_message_size:
                  a, b, c, d = my_split(
                      xs[i],
                      [
                          i - backward_message_size,
                          backward_message_size,
                          1,
                          n - i - 1,
                      ],
                  )
                  backward_messages[i] = b
                  parts[i][i] = c
                  forward_messages[i] = tf.concat(
                      [d, a], axis=split_axis
                  )
              else:
                  a, b, c, d = my_split(
                      xs[i],
                      [
                          i,
                          1,
                          forward_message_size,
                          backward_message_size - i,
                      ],
                  )
                  backward_messages[i] = tf.concat(
                      [d, a], axis=split_axis
                  )
                  parts[i][i] = b
                  forward_messages[i] = c
      for step in xrange(
          1, max(forward_message_size, backward_message_size) + 1
      ):
          new_forward_messages = [None] * n
          new_backward_messages = [None] * n
          for i in xrange(n):
              with tf.device(devices[i]):
                  if forward_message_size > 0:
                      (
                          parts[i][(i - step) % n],
                          new_forward_messages[i],
                      ) = my_split(
                          forward_messages[(i - 1) % n],
                          [1, forward_message_size - 1],
                      )
                  if backward_message_size > 0:
                      (
                          new_backward_messages[i],
                          parts[i][(i + step) % n],
                      ) = my_split(
                          backward_messages[(i + 1) % n],
                          [backward_message_size - 1, 1],
                      )
          forward_message_size -= 1
          backward_message_size -= 1
          forward_messages = new_forward_messages
          backward_messages = new_backward_messages
      return mtf.parallel(
          devices, tf.concat, parts, axis=[concat_axis] * n
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import functools
  import os
  import random
  from mesh_tensorflow import ops_with_redefined_builtins
  from six.moves import xrange
  import tensorflow.compat.v1
label_desc: Use image node_id 5 for calling a global function with example usage: alltoall_ring(xs, devices, split_axis, concat_axis) and returns: mtf, xs, tf